CN111618655A - Quantitative evaluation method for health degree of ball screw of numerical control machine tool - Google Patents
Quantitative evaluation method for health degree of ball screw of numerical control machine tool Download PDFInfo
- Publication number
- CN111618655A CN111618655A CN201911204656.4A CN201911204656A CN111618655A CN 111618655 A CN111618655 A CN 111618655A CN 201911204656 A CN201911204656 A CN 201911204656A CN 111618655 A CN111618655 A CN 111618655A
- Authority
- CN
- China
- Prior art keywords
- index
- ball screw
- formula
- health
- machine tool
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000036541 health Effects 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000011158 quantitative evaluation Methods 0.000 title claims abstract description 5
- 230000003595 spectral effect Effects 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 7
- 238000007781 pre-processing Methods 0.000 claims description 7
- 230000007613 environmental effect Effects 0.000 claims description 6
- YBJHBAHKTGYVGT-ZKWXMUAHSA-N (+)-Biotin Chemical compound N1C(=O)N[C@@H]2[C@H](CCCCC(=O)O)SC[C@@H]21 YBJHBAHKTGYVGT-ZKWXMUAHSA-N 0.000 claims description 2
- 230000002159 abnormal effect Effects 0.000 claims description 2
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 claims description 2
- FEPMHVLSLDOMQC-UHFFFAOYSA-N virginiamycin-S1 Natural products CC1OC(=O)C(C=2C=CC=CC=2)NC(=O)C2CC(=O)CCN2C(=O)C(CC=2C=CC=CC=2)N(C)C(=O)C2CCCN2C(=O)C(CC)NC(=O)C1NC(=O)C1=NC=CC=C1O FEPMHVLSLDOMQC-UHFFFAOYSA-N 0.000 claims description 2
- 238000005299 abrasion Methods 0.000 claims 1
- 238000012423 maintenance Methods 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000003754 machining Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
- B23Q17/007—Arrangements for observing, indicating or measuring on machine tools for managing machine functions not concerning the tool
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
- B23Q17/007—Arrangements for observing, indicating or measuring on machine tools for managing machine functions not concerning the tool
- B23Q17/008—Life management for parts of the machine
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Numerical Control (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention discloses a health degree quantitative evaluation method for a ball screw of a numerical control machine tool; s1, removing noise from the collected vibration signal and voltage signal generated by the ball screw in the working state of the machine tool, calculating a characteristic index, and obtaining the current health index of the equipment according to a formula; s2, substituting the health index into a formula solution equation to obtain the service life of the ball screw of the current equipment; and S3, putting the service life into a formula to obtain a health prediction formula of the equipment. The invention can predict the health condition of the ball screw of the machine tool and quantitatively display the health condition, prevent equipment failure in advance, prolong the working period of the equipment, avoid economic loss of factories and the like caused by untimely maintenance of parts, can be suitable for various working environments and has wide applicability.
Description
Technical Field
The invention relates to the technical field of numerical control machining and data analysis, in particular to a method for quantitatively evaluating the health degree of a ball screw of a numerical control machine tool.
Background
The ball screw is an important precise accessory of a machine tool, has an obvious function of guaranteeing the processing precision in the processing, the aging and health degree of the ball screw directly influence the yield of products, faults generated by the ball screw are various, a fixed mode is absent, gradual faults exist, and the faults are more serious along with the use time; sometimes a sudden failure, generally without noticeable symptoms. The faults are generated by various adverse factors and external combined action, once the faults occur, the quality of products is influenced, machine tool shutdown and factory shutdown are seriously caused, and the production of enterprises is seriously influenced. Therefore, a method capable of effectively detecting and predicting the health of the ball screw is required to be found, indexes such as the residual service life of machine parts are predicted, real-time detection of the indexes is realized, faults are prevented in advance, the health degree is quantitatively evaluated, maintenance decisions are facilitated, the product quality is guaranteed, and the probability of machine shutdown and factory shutdown is reduced.
Disclosure of Invention
In view of the above, the present invention provides a method for quantitatively evaluating the health degree of a ball screw of a numerically-controlled machine tool, which is used for establishing a predictive formula to predict the health state of a spindle in real time by performing data acquisition and analysis on the ball screw of the machine tool, so as to effectively solve the problems existing in the above technical background.
S1, preprocessing the collected vibration signals and current signals generated by the ball screw in the working state of the machine tool, then calculating a characteristic index, and obtaining the current health index of the equipment according to a formula;
s2, substituting the health index into a formula solution equation to obtain the service life t of the ball screw of the current equipment;
and S3, putting the service life into a formula to obtain a health prediction formula of the equipment.
The method for preprocessing the S1 signal is as follows:
s11, preprocessing the data, filling missing values and modifying abnormal values;
s12, calculating the characteristic index by using the processed data, which comprises the following steps:
s121, calculating the following data characteristics: the average value of the vibration signal, the standard deviation of the vibration signal, the kurtosis of the vibration signal, the wave form factor of the vibration signal, the margin factor of the vibration signal, the spectral kurtosis of the vibration signal, the standard deviation of the spectral kurtosis and the average value of the voltage signal;
s122, substituting the data characteristics into a formula to obtain an instantaneous impact index SI, a missing wear index AI and an environmental interference index RI;
s123, substituting the instantaneous impact index SI, the missing wear index AI and the environmental interference index RI into a formula to obtain an equipment damage degree index S;
s13, substituting the equipment damage degree index S into a formula to obtain the current health index h of the equipment, wherein the formula is that h is 100-S
Further, a method for quantitatively evaluating health degree of ball screw of a numerical control machine tool is characterized in that in step S122, the instantaneous impact index SI is obtained by substituting a kurtosis K of a vibration signal, a form factor S of the vibration signal, a spectral kurtosis St of the vibration signal, and a standard deviation Sts of the spectral kurtosis into a formula, wherein the formula is as follows:
wherein C isT、CFTime domain correction parameters and frequency domain correction parameters, respectively
The missing wear index AI is represented by the margin factor I of the vibration signal with the following formula:
AI＝CI∑I2in which C isITo correct the coefficient for impact
The environmental interference index RI is calculated by substituting the mean value U of the voltage signal, the mean value U of the vibration signal and the standard deviation s of the vibration signal into the following formula;
RI＝CUU+ln(|u|+1)+CSs, wherein CUTo correct the coefficient C for the voltageSFor correcting the coefficient for deviation
Further, a method for quantitatively evaluating the health degree of the ball screw of the numerical control machine tool is characterized in that in step S122, the formula is as follows:
s＝AI×(SI+C×N-1) + RI, C is the basic dynamic rated load (N) of the equipment
Further, the method for quantitatively evaluating the health degree of the ball screw of the numerical control machine is characterized in that in the step S2, the formula is as follows:
wherein L is the service life, C1, N, m and K are correction coefficients, C is the basic dynamic load (N), fw is the load coefficient, F is the bearing load (N), N is the number of round trips per minute, and L is the stroke length (mm).
Furthermore, the method for quantitatively evaluating the health degree of the ball screw of the numerical control machine tool is characterized in that in the step S3, the formulas are as follows, wherein H is f (T + T), and Rt is L-T
Wherein T is the time from the current time to the prediction point, H is the health prediction value after the time T, and Rt is the residual life of the ball screw.
The invention has the advantages of
The invention can effectively predict the health index of the ball screw of the numerical control machine tool, quantitatively display the health index, assist decision-making, prepare maintenance accessories in advance, avoid loss caused by long-time shutdown, cover multiple links aiming at numerical control machines with different styles in an intelligent factory, improve the compatibility of equipment, realize unified prediction of the residual life of various numerical control machines and provide a more optimized maintenance and management scheme.
The method has the advantages of simple operation, convenient parameter modification, easy formula understanding, no need of strong mathematical capacity and suitability for various working environments.
Drawings
FIG. 1 is a flow chart of the steps of a quantitative evaluation method for the health degree of a ball screw of a numerical control machine tool;
FIG. 2-1 is a large window diagram of vibration original signals of a ball screw of a numerically controlled machine tool;
FIG. 2-2 is a small window diagram of vibration original signals of a ball screw of the numerical control machine;
FIG. 3 is a diagram of the voltage raw signal of the ball screw of the numerically controlled machine tool;
FIG. 4 is a full life cycle health index prediction curve for a ball screw of a numerically controlled machine tool;
FIG. 5 is a health index prediction curve for a numerically controlled machine tool ball screw;
Detailed Description
The invention will be further described by the following specific examples in conjunction with the drawings, which are provided for illustration only and are not intended to limit the scope of the invention.
S1, removing noise from the collected vibration signal and voltage signal generated by the ball screw in the working state of the machine tool, calculating a characteristic index, and obtaining the current health index of the equipment according to a formula;
in the step S1, the parameter CT＝1.5CF＝0.78CI＝2.31×10-4CU＝0.17CS＝1.58
The AI is calculated to be 3.32 × 10-3SI＝1062 RI＝1.03
C＝1850N s＝AI×(SI+C×N-1)+RI＝10.7
The health index h obtained by calculation is 89.3;
s2, substituting the health index into a formula solution equation to obtain the service life of the ball screw of the current equipment;
in step S2, the service life calculated by C1 ═ 21, N ═ 3.54, m ═ 3.5, L ═ 12600, and K ═ 71 is 3700 (h);
s3, putting the service life into a formula to obtain a health prediction formula of the equipment, wherein in the step S3, the formula for obtaining the residual service life is as follows:
wherein T is the time between the current point and the predicted point, H is the predicted health value after the time T, and Rt is the remaining life (H) of the ball screw.
As shown in fig. 4 and 5, as the used service life increases and the remaining service life decreases, the health index of the ball screw is continuously decreased, and the health degree of the ball screw can be intuitively predicted and displayed.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. A quantitative evaluation method for the health degree of a ball screw of a numerical control machine tool is characterized by comprising the following specific steps:
s1, preprocessing the collected vibration signals and voltage signals generated by the ball screw in the working state of the machine tool, then calculating a characteristic index, and obtaining the current health index of the equipment according to a formula;
s2, substituting the health index into a formula solution equation to obtain the service life t of the ball screw of the current equipment;
and S3, putting the service life into a formula to obtain a health prediction formula of the equipment.
2. The method for quantitatively evaluating the health degree of the ball screw of the numerical control machine tool according to claim 1, wherein the steps of preprocessing the vibration signal and the voltage signal and calculating the characteristic index and the equipment health index of S1 are as follows:
s11, preprocessing the vibration signal and the voltage signal generated by the ball screw when the machine tool is in the working state,
s12, calculating a characteristic index by using the processed data;
and S13, substituting the equipment damage degree index S into a formula to obtain the current health index h of the equipment, wherein the formula is h-100-S.
3. The method for quantitatively evaluating the health degree of the ball screw of the numerical control machine tool according to claim 2, wherein the signal preprocessing method of S11 is to preprocess data, fill in missing values, and modify abnormal values; in S12, the specific steps of calculating the characteristic index using the processed data are as follows:
s121, calculating the following data characteristics: the average value of the vibration signal, the standard deviation of the vibration signal, the kurtosis of the vibration signal, the wave form factor of the vibration signal, the margin factor of the vibration signal, the spectral kurtosis of the vibration signal, the standard deviation of the spectral kurtosis and the average value of the voltage signal;
s122, substituting the data characteristics into a formula to obtain an instantaneous impact index SI, a missing wear index AI and an environmental interference index RI;
and S123, substituting the instantaneous impact index SI, the missing wear index AI and the environmental interference index RI into a formula to obtain an equipment damage degree index S.
4. The method as claimed in claim 3, wherein the instantaneous impact index SI in step S122 is obtained by substituting the kurtosis K of the vibration signal, the form factor S of the vibration signal, the spectral kurtosis St of the vibration signal, and the standard deviation St of the spectral kurtosis into a formula:
wherein C isT、CFTime domain correction parameters and frequency domain correction parameters, respectively
The missing abrasion index AI is represented by a margin factor I of the vibration signal as the following formula:
AI＝CI∑I2in which C isITo correct the coefficient for impact
The environmental interference index RI is calculated by substituting the mean value U of the voltage signal, the mean value U of the vibration signal and the standard deviation s of the vibration signal into the following formula;
RI＝CUU+ln(|u|+1)+CSs, wherein CUTo correct the coefficient C for the voltageSIs a deviation correction coefficient.
5. The method for quantitatively evaluating the health degree of the ball screw of the numerical control machine tool according to claim 3, wherein the formula of the step S123 is as follows:
s＝AI×(SI+C×N-1) + RI, C is the basic dynamic load rating (N) of the device.
6. The method for quantitatively evaluating the health degree of the ball screw of the numerical control machine tool according to claim 1, wherein the step S2 has the following formula:
wherein L is the working life, C1, N, m are correction coefficients, C is the basic dynamic load (N), fw is the load coefficient, F is the bearing load (N), N is the number of round trips per minute, and L is the stroke length (mm).
7. The method for quantitatively evaluating the health degree of the ball screw of the numerical control machine tool according to claim 1, wherein the step S3 is represented by the following formula:
H＝f(T+t),Rt＝L-t
wherein T is the time from the current time to the prediction point, H is the health prediction value after the time T, and Rt is the residual life of the ball screw.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911204656.4A CN111618655B (en) | 2019-11-29 | 2019-11-29 | Quantitative evaluation method for health degree of ball screw of numerical control machine tool |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911204656.4A CN111618655B (en) | 2019-11-29 | 2019-11-29 | Quantitative evaluation method for health degree of ball screw of numerical control machine tool |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111618655A true CN111618655A (en) | 2020-09-04 |
| CN111618655B CN111618655B (en) | 2021-12-28 |
Family
ID=72255900
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911204656.4A Active CN111618655B (en) | 2019-11-29 | 2019-11-29 | Quantitative evaluation method for health degree of ball screw of numerical control machine tool |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111618655B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113642618A (en) * | 2021-07-27 | 2021-11-12 | 上海展湾信息科技有限公司 | Method and equipment for state prediction model training of screw device |
| CN117451348A (en) * | 2023-12-26 | 2024-01-26 | 宁德时代新能源科技股份有限公司 | Screw device detection method and device, electronic device and storage medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101870075A (en) * | 2010-07-02 | 2010-10-27 | 西南交通大学 | Method for predicting service life of screw pair of numerical control machine on basis of performance degradation model |
| CN102435436A (en) * | 2011-11-24 | 2012-05-02 | 电子科技大学 | Method for evaluating degradation of state of fan bearing |
| JP2014223700A (en) * | 2013-05-16 | 2014-12-04 | ファナック株式会社 | Numerical control device provided with function of estimating life of bearing for supporting spindle |
| CN107357967A (en) * | 2017-06-21 | 2017-11-17 | 东北大学 | A kind of extracting method of ball-screw loading spectrum |
| KR101802701B1 (en) * | 2017-04-06 | 2017-11-28 | 울산대학교 산학협력단 | Bearing reverse design method and device by evaluating residual life of bearing |
| CN109211564A (en) * | 2018-10-24 | 2019-01-15 | 哈工大机器人（山东）智能装备研究院 | A kind of adaptive thresholding value detection method for ball screw assembly, health evaluating |
| CN109255395A (en) * | 2018-10-24 | 2019-01-22 | 哈工大机器人（山东）智能装备研究院 | A kind of life-span prediction method of ball screw assembly, |
| CN109255201A (en) * | 2018-10-24 | 2019-01-22 | 哈工大机器人（山东）智能装备研究院 | A kind of ball screw assembly, health evaluating method based on SOM-MQE |
| CN109271741A (en) * | 2018-10-25 | 2019-01-25 | 北京航空航天大学 | A kind of prediction of buck DC-DC power module remaining life and health evaluating method |
| CN110046409A (en) * | 2019-03-29 | 2019-07-23 | 西安交通大学 | A kind of components of steam turbine health state evaluation method based on ResNet |
-
2019
- 2019-11-29 CN CN201911204656.4A patent/CN111618655B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101870075A (en) * | 2010-07-02 | 2010-10-27 | 西南交通大学 | Method for predicting service life of screw pair of numerical control machine on basis of performance degradation model |
| CN102435436A (en) * | 2011-11-24 | 2012-05-02 | 电子科技大学 | Method for evaluating degradation of state of fan bearing |
| JP2014223700A (en) * | 2013-05-16 | 2014-12-04 | ファナック株式会社 | Numerical control device provided with function of estimating life of bearing for supporting spindle |
| KR101802701B1 (en) * | 2017-04-06 | 2017-11-28 | 울산대학교 산학협력단 | Bearing reverse design method and device by evaluating residual life of bearing |
| CN107357967A (en) * | 2017-06-21 | 2017-11-17 | 东北大学 | A kind of extracting method of ball-screw loading spectrum |
| CN109211564A (en) * | 2018-10-24 | 2019-01-15 | 哈工大机器人（山东）智能装备研究院 | A kind of adaptive thresholding value detection method for ball screw assembly, health evaluating |
| CN109255395A (en) * | 2018-10-24 | 2019-01-22 | 哈工大机器人（山东）智能装备研究院 | A kind of life-span prediction method of ball screw assembly, |
| CN109255201A (en) * | 2018-10-24 | 2019-01-22 | 哈工大机器人（山东）智能装备研究院 | A kind of ball screw assembly, health evaluating method based on SOM-MQE |
| CN109271741A (en) * | 2018-10-25 | 2019-01-25 | 北京航空航天大学 | A kind of prediction of buck DC-DC power module remaining life and health evaluating method |
| CN110046409A (en) * | 2019-03-29 | 2019-07-23 | 西安交通大学 | A kind of components of steam turbine health state evaluation method based on ResNet |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113642618A (en) * | 2021-07-27 | 2021-11-12 | 上海展湾信息科技有限公司 | Method and equipment for state prediction model training of screw device |
| CN113642618B (en) * | 2021-07-27 | 2024-03-01 | 上海展湾信息科技有限公司 | Method and equipment for training screw device state prediction model |
| CN117451348A (en) * | 2023-12-26 | 2024-01-26 | 宁德时代新能源科技股份有限公司 | Screw device detection method and device, electronic device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111618655B (en) | 2021-12-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109909804B (en) | Tool wear damage online monitoring method based on spindle driving current and process steps | |
| CN109766334B (en) | Processing method and system for online monitoring abnormal data of power equipment | |
| CN111618655B (en) | Quantitative evaluation method for health degree of ball screw of numerical control machine tool | |
| CN101870076B (en) | Method for predicting service life of guide pair of numerical control machine on basis of performance degradation model | |
| CN107247230B (en) | Rotary motor state monitoring method based on support vector machine and data driving | |
| CN107186547A (en) | Numerical control turning batch machining tool wear on-line monitoring method based on cutting power | |
| EP1451550B1 (en) | System and method for identifying the presence of a defect in vibrating machinery | |
| CN108427841B (en) | Real-time prediction method for residual life of numerical control machine tool cutter | |
| CN112947290B (en) | Edge cloud cooperation-based equipment state monitoring method and system and storage medium | |
| CN109821900B (en) | Online detection method for working abnormity of rolling mill transmission equipment | |
| CN110836696A (en) | Remote fault prediction method and system suitable for phase modulator system | |
| Liu et al. | Data-driven prognostics of remaining useful life for milling machine cutting tools | |
| KR20200049295A (en) | A method to predict health index transition and residual life for turbomachinery | |
| Shao et al. | Gearbox vibration monitoring using extended Kalman filters and hypothesis tests | |
| CN116408501A (en) | On-machine unsupervised real-time monitoring method for hob abrasion state in channeling mode | |
| CN114326593B (en) | Cutter life prediction system and method | |
| CN114925752A (en) | Method for monitoring abnormal cutting of plate of linear cutting machine based on Internet of things | |
| CN111176226A (en) | Automatic analysis method for alarm threshold of equipment characteristic parameter based on operation condition | |
| CN112100577A (en) | Long-range correlation-based equipment operation stability online monitoring method and system | |
| CN113632025A (en) | Methods, systems, and computer program products for assessing energy consumption in an industrial environment | |
| US10656637B2 (en) | Monitoring system and monitoring method | |
| CN113654651B (en) | Method for extracting early degradation features of strong robust signal and monitoring running state of equipment | |
| CN111623105B (en) | Industrial robot RV reducer health degree quantitative evaluation method | |
| CN111625907B (en) | Quantitative evaluation method for health degree of numerical control machine tool spindle | |
| CN112621381A (en) | Intelligent health state evaluation method and device for machine tool feeding system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: Building 7, No. 124 Dongbao Road, Dongcheng Street, Dongguan City, Guangdong Province, 523128 Patentee after: Guangdong Prophet Big Data Co.,Ltd. Country or region after: China Address before: 523128 R & D building, No. 2, Weifeng Road, Waijing Industrial Park, Niushan community, Dongcheng Street, Dongguan City, Guangdong Province Patentee before: Dongguan prophet big data Co.,Ltd. Country or region before: China |
|
| CP03 | Change of name, title or address |